A detailed description of SCIP can be found in

 The nonlinear solving features are described in

Here are the MPS files assembled as an archive: .

Ambros Gleixner, Leon Eifler, Tristan Gally, Gerald Gamrath, Patrick Gemander, Robert Lion Gottwald, Gregor Hendel, Christopher Hojny, Thorsten Koch, Matthias Miltenberger, Benjamin Müller, Marc E. Pfetsch, Christian Puchert, Daniel Rehfeldt, Franziska Schlösser, Felipe Serrano, Yuji Shinano, Jan Merlin Viernickel, Stefan Vigerske, Dieter Weninger, Jonas T. Witt, Jakob Witzig
ZIB-Report 17-61, Zuse Institute Berlin, December 2017

. Previous releases and versions for different platforms are available .

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The main goal of my thesis was to cross the barrier between the fields of optimization and data analysis by applying methods from data analysis to optimization problems. In particular, I applied data analysis techniques to obtain information about black box functions, i.e. functions for which we do not know an algebraic expression.

I presented both an algorithm and a reference implementation to solve optimization problems where: using methods from data analysis: The algorithm is a so-called incomplete global optimizer, i.e. it attempts to find a global solution for the optimization problem, but is unable to guarantee globality. In fact, it cannot even guarantee always finding a local optimum, due to the lack of gradients and any sort of global information. Despite this lack of guarantees, the algorithm performs well in practice.

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SCIP is a framework for Constraint Integer Programming oriented towards the needs of Mathematical Programming experts who want to have total control of the solution process and access detailed information down to the guts of the solver. SCIP can also be used as a pure MIP solver or as a framework for branch-cut-and-price.

Black Box Optimization with Data Analysis

SCIP is implemented as C callable library and provides C++ wrapper classes for user plugins. It can also be used as a standalone program to solve mixed integer programs given in MPS, LP, flatzinc, CNF, OPB, WBO, PIP, or CIP format. Besides that SCIP can directly read models.

Black Box Optimization with Data Analysis by Kevin Kofler

The SCIP Optimization Suite is a toolbox for generating and solving mixed integer nonlinear programs, in particular mixed integer linear programs, and constraint integer programs. It consists of the following parts:

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The user can easily generate linear, mixed integer and mixed integer quadratically constrained programs with the modeling language ZIMPL. The resulting model can directly be loaded into SCIP and solved. In the solution process SCIP may use SoPlex as underlying LP solver.

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Since all five components are available in source code and free for academic use, they are an ideal tool for academic research purposes and for teaching mixed integer programming.

Note to students: MTH is now MST

SCIP is distributed under the . You are allowed to retrieve SCIP for research purposes as a member of a non-commercial and academic institution.
If you want to use SCIP commercially or if you are interested in maintenance and support, please contact us by sending an email to .